5B.4 Clark County Climate Variability and Global Climate Model Selection Process with Southern Nevada Water Agency

Tuesday, 9 January 2018: 11:15 AM
Room 6B (ACC) (Austin, Texas)
Julie Kalansky, Scripps Institution of Oceanography, La Jolla, CA; and A. M. Sheffield, K. Brooks, J. Johnson, D. Cayan, and D. W. Pierce

The Las Vegas metropolitan area in southern Nevada is one of the most arid cities in the U.S., with summer temperatures regularly above 105° F and only 4.2 inches of annual precipitation. We, the California Nevada Applications Program, (CNAP, a NOAA RISA team) are working collaboratively with the Southern Nevada Water Authority (SNWA), which is the wholesale drinking water supplier in the region serving approximately 2 million residents and 40 million visitors annually, to understand the spatial and temporal climate variability of the region to aid selection of a set of representative future climate projections. Using the methodology of Cayan et al. (2015), historical temporal and spatial variability of maximum temperature (Tmax), minimum temperature (Tmin) and total precipitation (P) data was used to cull 32 climate models down to six that best represent Clark County climate, and examine future projections from this smaller subset. Three evaluation metrics were used to select the models: number of wet days in summer and winter and Tmax variability within a month and atmospheric patterns of extremes. The number of wet days and Tmax variability influence SNWA water demand in the region, the latter captures the atmospheric processes driving extremes that if intensified could impact SNWA infrastructure in the future. Continuous dialogue via biweekly conference calls ensured the collaboration balanced the needs of SNWA with what could be provided from the climate models. Six models were selected based on what SNWA saw as a realistic number for analysis, and the recommended best practice to use multiple models. The selected models included ACCESS-1.0, CCSM4, CMCC-CMS, CNRM-CM5, HadGEM2-ES, MPI-ESM-LR. Evaluation of Tmax, Tmin and precipitation projections suggest that fall season may have the largest change in temperature of all seasons and spring may become drier. The wide range of projections for precipitation indicates the uncertainty with projected changes in precipitation in the region. This user-driven climate analysis demonstrates a successful model of collaboration to produc
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